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Photoshop Artistic filters: Colored Pencils, Cutout, Dry Brush, Fine Grain, Fresco

PCWorld

The best thing about Photoshop is the huge selection of special effects called filters. But the real genius comes when you learn to mix them into new and even more astounding effects, such as Fresco and Paint Daubs together, or Poster Edges with Watercolors. The purpose of this article is to introduce, explain, and show examples of these amazing features. In a nutshell, Photoshop Artistic filters are computerized, artistic techniques (or special effects) that enable you to create images that simulate artistic styles such as colored pencils, watercolors, chalk pastels, charcoal, pen and inks, crayons, and dozens of other artistic media. In the current version of PS, there are over 225 special effects filters.


Nobias - Track media bias, credibility, authenticity, and politics in the press you read. Burst your filter bubble.

#artificialintelligence

Nobias provides insights on any online article before you read it. These insights include: the various biases of the article along with the credibility of the author and the source to keep fake news in check.


A Model for Learned Bloom Filters, and Optimizing by Sandwiching

arXiv.org Machine Learning

Recent work has suggested enhancing Bloom filters by using a pre-filter, based on applying machine learning to determine a function that models the data set the Bloom filter is meant to represent. Here we model such learned Bloom filters,, with the following outcomes: (1) we clarify what guarantees can and cannot be associated with such a structure; (2) we show how to estimate what size the learning function must obtain in order to obtain improved performance; (3) we provide a simple method, sandwiching, for optimizing learned Bloom filters; and (4) we propose a design and analysis approach for a learned Bloomier filter, based on our modeling approach.


Finite Difference Neural Networks: Fast Prediction of Partial Differential Equations

arXiv.org Machine Learning

Discovering the underlying behavior of complex systems is an important topic in many science and engineering disciplines. In this paper, we propose a novel neural network framework, finite difference neural networks (FD-Net), to learn partial differential equations from data. Specifically, our proposed finite difference inspired network is designed to learn the underlying governing partial differential equations from trajectory data, and to iteratively estimate the future dynamical behavior using only a few trainable parameters. We illustrate the performance (predictive power) of our framework on the heat equation, with and without noise and/or forcing, and compare our results to the Forward Euler method. Moreover, we show the advantages of using a Hessian-Free Trust Region method to train the network.


How to hide the Instagram filters you hate

Mashable

Are you partial to Hefe but hate Moon? If you get fed up of swiping past endless Instagram filters in order to select the one you want, we can help. There's an easy way to hide the Instagram filters you don't use and rearrange the ones you do like into an order that suits you. Read on for our simple how-to and get set to tweak your app to streamline your future Instagram experience. Once you've launched Insta and snapped a pic, shot a video or selected either from your phone's media gallery, hit the Next option at the top right.